Associate-Cloud-Engineer Prüfungsaufgaben & Associate-Cloud-Engineer Schulungsunterlagen

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Google Associate-Cloud-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring successful operation of a cloud solution	20%	- Managing cost and performance 1. Analyze cost reports 2. Optimize resource usage - Managing resources 1. Troubleshoot common issues 2. Scale resources 3. Update configurations - Monitoring and logging 1. Use Cloud Monitoring 2. Use Cloud Logging 3. Set up alerts
Deploying and implementing a cloud solution	25%	- Deploying and implementing Google Kubernetes Engine resources 1. Create and manage clusters 2. Deploy workloads 3. Configure networking for GKE - Deploying and implementing data solutions 1. Provision databases 2. Set up data processing pipelines - Deploying and implementing Compute Engine resources 1. Configure access and security 2. Launch virtual machines 3. Create managed instance groups 4. Manage images and snapshots - Deploying and implementing serverless resources 1. Deploy Cloud Run services 2. Create Cloud Functions
Planning and configuring a cloud solution	17.5%	- Planning and configuring compute resources 1. Plan instance configuration 2. Choose storage options 3. Select compute options 4. Plan data storage solutions - Planning and configuring storage resources 1. Plan data durability and availability 2. Select storage classes - Planning and configuring network resources 1. Design VPC networks 2. Plan hybrid connectivity 3. Plan firewall rules 4. Choose load balancing options
Setting up a cloud solution environment	20%	- Managing billing configuration 1. Configure billing exports 2. Create and manage billing accounts 3. Set budgets and alerts 4. Link projects to billing accounts - Setting up cloud projects and accounts 1. Enable APIs 2. Apply organizational policies 3. Create resource hierarchy 4. Manage IAM roles and permissions 5. Manage quotas and limits 6. Manage users and groups
Configuring access and security	17.5%	- Managing IAM 1. Manage service accounts 2. Define roles and permissions 3. Implement least privilege access - Managing data security 1. Enable encryption 2. Manage access to data - Configuring network security 1. Secure VPC networks 2. Manage security controls 3. Configure private access

>> Associate-Cloud-Engineer Prüfungsaufgaben <<

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Google Associate Cloud Engineer Exam Associate-Cloud-Engineer Prüfungsfragen mit Lösungen (Q248-Q253):

248. Frage
You need to set up permissions for a set of Compute Engine instances to enable them to write data into a particular Cloud Storage bucket. You want to follow Google-recommended practices.
What should you do?

A. Create a service account with an access scope.
Use the access scope `https://www.googleapis.com/ auth/cloud-platform'.
B. Create a service account and add it to the IAM role `storage.objectCreator' for that bucket.
C. Create a service account and add it to the IAM role `storage.objectAdmin' for that bucket.
D. Create a service account with an access scope.
Use the access scope `https://www.googleapis.com/ auth/devstorage.write_only'.

Antwort: A

249. Frage
Your company is moving from an on-premises environment to Google Cloud Platform (GCP). You have multiple development teams that use Cassandra environments as backend databases. They all need a development environment that is isolated from other Cassandra instances. You want to move to GCP quickly and with minimal support effort. What should you do?

A. 1. Build a Cassandra Compute Engine instance and take a snapshot of it.
2. Upload the snapshot to Cloud Storage and make it accessible to your developers.
3. Build instructions to create a Compute Engine instance from the snapshot so that developers can do it themselves.
B. 1. Build a Cassandra Compute Engine instance and take a snapshot of it.
2. Use the snapshot to create instances for your developers.
C. 1. Advise your developers to go to Cloud Marketplace.
2. Ask the developers to launch a Cassandra image for their development work.
D. 1. Build an instruction guide to install Cassandra on GCP.
2. Make the instruction guide accessible to your developers.

Antwort: C

Begründung:
You can deploy Cassandra as a Service, called Astra, on the Google Cloud Marketplace. Not only do you get a unified bill for all GCP services, your Developers can now create Cassandra clusters on Google Cloud in minutes and build applications with Cassandra as a database as a service without the operational overhead of managing Cassandra.

250. Frage
Your company runs its Linux workloads on Compute Engine instances. Your company will be working with a new operations partner that does not use Google Accounts. You need to grant access to the instances to your operations partner so they can maintain the installed tooling. What should you do?

A. Ask the operations partner to generate SSH key pairs, and add the public keys to the VM instances.
B. Set up Cloud VPN between your Google Cloud VPC and the internal network of the operations partner.
C. Enable Cloud IAP for the Compute Engine instances, and add the operations partner as a Cloud IAP Tunnel User.
D. Tag all the instances with the same network tag. Create a firewall rule in the VPC to grant TCP access on port 22 for traffic from the operations partner to instances with the network tag.

Antwort: D

251. Frage
You need to select and configure compute resources for a set of batch processing jobs. These jobs take around 2 hours to complete and are run nightly. You want to minimize service costs. What should you do?

A. Select Compute Engine. Use preemptible VM instances of the appropriate standard machine type.
B. Select Google Kubernetes Engine. Use a three-node cluster with micro instance type.
C. Select Google Kubernetes Engine. Use a single-node culster with a small instance type.
D. Select Compute Engine. Use VM instance types that support micro bursting.

Antwort: A

252. Frage
You are operating a Google Kubernetes Engine (GKE) cluster for your company where different teams can run non-production workloads. Your Machine Learning (ML) team needs access to Nvidia Tesla P100 GPUs to train their models. You want to minimize effort and cost. What should you do?

A. Ask your ML team to add the "accelerator: gpu" annotation to their pod specification.
B. Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.
C. Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.
D. Recreate all the nodes of the GKE cluster to enable GPUs on all of them.

Antwort: B

Begründung:
Explanation
This is the most optimal solution. Rather than recreating all nodes, you create a new node pool with GPU enabled. You then modify the pod specification to target particular GPU types by adding node selector to your workloads Pod specification. YOu still have a single cluster so you pay Kubernetes cluster management fee for just one cluster thus minimizing the cost.Ref: https://cloud.google.com/kubernetes-engine/docs/how-to/gpusRef: https://cloud.google.com/kubernetes Example:
apiVersion: v1
kind: Pod
metadata:
name: my-gpu-pod
spec:
containers:
name: my-gpu-container
image: nvidia/cuda:10.0-runtime-ubuntu18.04
command: [/bin/bash]
resources:
limits:
nvidia.com/gpu: 2
nodeSelector:
cloud.google.com/gke-accelerator: nvidia-tesla-k80 # or nvidia-tesla-p100 or nvidia-tesla-p4 or nvidia-tesla-v100 or nvidia-tesla-t4

253. Frage
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